Need	Skill	Content
Reliability patterns	`etl-core-patterns`	Idempotency, checkpointing, error handling, chunking, retry, logging
Load strategies	`etl-incremental-patterns`	Backfill, timestamp-based, CDC, pipeline orchestration

Pattern Selection Guide

By Reliability Need

Need	Pattern	Skill
Repeatable runs	Idempotency	`etl-core-patterns`
Resume after failure	Checkpointing	`etl-core-patterns`
Handle bad records	Error handling + DLQ	`etl-core-patterns`
Memory management	Chunked processing	`etl-core-patterns`
Network resilience	Retry with backoff	`etl-core-patterns`
Observability	Structured logging	`etl-core-patterns`

By Load Strategy

Scenario	Pattern	Skill
Small tables (<100K)	Full refresh	`etl-incremental-patterns`
Large tables	Timestamp incremental	`etl-incremental-patterns`
Real-time sync	CDC events	`etl-incremental-patterns`
Historical migration	Parallel backfill	`etl-incremental-patterns`
Zero-downtime refresh	Swap pattern	`etl-incremental-patterns`
Multi-step pipelines	Pipeline orchestration	`etl-incremental-patterns`

Quick Reference

Idempotency Options

# Small datasets: Delete-then-insert
# Large datasets: UPSERT on conflict
# Change detection: Row hash comparison

Load Strategy Decision

Is table < 100K rows?
  → Full refresh

Has reliable timestamp column?
  → Timestamp incremental

Source supports CDC?
  → CDC event processing

Need zero downtime?
  → Swap pattern (temp table → rename)

One-time historical load?
  → Parallel backfill with date ranges

Common Pipeline Structure

# 1. Setup
checkpoint = Checkpoint('.etl_checkpoint.json')
processor = ETLProcessor()

# 2. Extract (with incremental)
df = incremental_by_timestamp(source_table, 'updated_at')

# 3. Transform (with error handling)
transformed = processor.process_batch(df.to_dict('records'))

# 4. Load (with idempotency)
upsert_records(pd.DataFrame(transformed))

# 5. Checkpoint
checkpoint.set_last_processed('sync', df['updated_at'].max())

# 6. Handle failures
processor.save_failures('failures/')

Related Skills

data-validation - Validate data quality during ETL
data-quality - Monitor data quality metrics
pandas-coder - DataFrame transformations

npx skills add smithery/majesticlabs-dev --skill etl-patterns